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Record W2974880470 · doi:10.14507/epaa.27.4211

The development of competencies required for school principals in Quebec: Adequacy between competency standard and practice

2019· article· en· W2974880470 on OpenAlexaffabout
Monique Lambert, Yamina Bouchamma

Bibliographic record

VenueEducation Policy Analysis Archives · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsVariety (cybernetics)Professional developmentPsychologyFunction (biology)Face (sociological concept)PedagogyWork (physics)Medical educationPolitical sciencePublic relationsSociologyMedicineEngineering

Abstract

fetched live from OpenAlex

School principals deal with a variety of increasingly complex responsibilities (Trudeau, 2013). They must therefore develop and hone professional competencies that will enable them to effectively perform their pedagogical, organizational, relational, or administrative duties (Barber, Whelan & Clark., 2010; Bisaillon et al., 2009; Darling-Hammond, Meyerson, LaPointe, & Orr, 2010; Pont, Nusche, & Moorman, 2008). In light of the many societal changes and resulting pressure on schools, many countries have developed competency standards for school principals to redefine the competencies required to successfully fulfill this role (UNESCO, 2006). Quebec has followed suit in this movement with the publication of its first competency standard for school principals in 2008. This document is used during the initial training for future school principals to develop competencies useful for this function. What competencies are needed by school leaders in relation to the challenges they face in their work? What are these challenges? Interviews with school principals (N = 13) allowed us to highlight challenges that arise in their day-to-day activities which allowed us to identify the professional and cross-curricular (behavioural) competencies that they implement to do their job.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.480
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2019
Admission routes2
Has abstractyes

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